code: def deepnn(x):
      """deepnn builds the graph for a deep net for classifying digits.
      Args:
      x: an input tensor with the dimensions (N_examples, 784), where 784 is the
      number of pixels in a standard MNIST image.
      Returns:
      A tuple (y, keep_prob). y is a tensor of shape (N_examples, 10), with values
      equal to the logits of classifying the digit into one of 10 classes (the
      digits 0-9).
      """
      # Reshape to use within a convolutional neural net.
      # Last dimension is for "features" - there is only one here, since images are
      # grayscale -- it would be 3 for an RGB image, 4 for RGBA, etc.
      with tf.name_scope("reshape"):
          x_image = tf.reshape(x, [-1, 28, 28, 1])
  
      # First convolutional layer - maps one grayscale image to 16 feature maps.
      with tf.name_scope("conv1"):
          W_conv1 = weight_variable([5, 5, 1, 16])
          b_conv1 = bias_variable([16])
          h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
  
      # Pooling layer - downsamples by 2X.
      with tf.name_scope("pool1"):
          h_pool1 = avg_pool_2x2(h_conv1)
  
      # Second convolutional layer -- maps 16 feature maps to 16.
      with tf.name_scope("conv2"):
          W_conv2 = weight_variable([5, 5, 16, 16])
          b_conv2 = bias_variable([16])
          h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
  
      # Second pooling layer.
      with tf.name_scope("pool2"):
          h_pool2 = avg_pool_2x2(h_conv2)
  
      # Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
      # is down to 256 feature maps -- maps this to 1024 features.
      with tf.name_scope("fc1"):
          W_fc1 = weight_variable([256, 100])
          b_fc1 = bias_variable([100])
  
          h_pool2_flat = tf.reshape(h_pool2, [-1, 256])
          h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
  
      # Map the 256 features to 10 classes, one for each digit
      with tf.name_scope("fc2"):
          W_fc2 = weight_variable([100, 10])
          b_fc2 = bias_variable([10])
  
          y_conv = tf.matmul(h_fc1, W_fc2) + b_fc2
      return y_conv
  
file_path: tf-encrypted/examples/deprecated/mnist_deep_cnn.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import os
  import sys
  import numpy
  import tensorflow
  from tensorflow.examples.tutorials.mnist import input_data
  from tensorflow.python.framework import graph_io
  from tensorflow.python.framework import graph_util
label_desc: Use image node_id 1 for calling a global function with example usage: deepnn(x) and returns: y_conv